Daniel@0: function [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
Daniel@0: % MK_HMM_BNET Make a (static( bnet to represent a hidden Markov model
Daniel@0: % [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
Daniel@0: %
Daniel@0: % T = num time slices
Daniel@0: % Q = num hidden states
Daniel@0: % O = size of the observed node (num discrete values or length of vector)
Daniel@0: % cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete
Daniel@0: % param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node
Daniel@0: 
Daniel@0: N = 2*T;
Daniel@0: dag = zeros(N);
Daniel@0: for i=1:T-1
Daniel@0:   dag(i,i+1)=1;
Daniel@0: end
Daniel@0: onodes = T+1:N;
Daniel@0: for i=1:T
Daniel@0:   dag(i, onodes(i)) = 1;
Daniel@0: end
Daniel@0: 
Daniel@0: if cts_obs
Daniel@0:   dnodes = 1:T;
Daniel@0: else
Daniel@0:   dnodes = 1:N;
Daniel@0: end
Daniel@0: ns = [Q*ones(1,T) O*ones(1,T)];
Daniel@0: 
Daniel@0: if param_tying
Daniel@0:   eclass = [1 2*ones(1,T-1) 3*ones(1,T)];
Daniel@0: else
Daniel@0:   eclass = 1:N;
Daniel@0: end
Daniel@0: 
Daniel@0: bnet = mk_bnet(dag, ns, dnodes, eclass);
Daniel@0: 
Daniel@0: hnodes = mysetdiff(1:N, onodes);
Daniel@0: if ~param_tying
Daniel@0:   for i=hnodes(:)'
Daniel@0:     bnet.CPD{i} = tabular_CPD(bnet, i);
Daniel@0:   end
Daniel@0:   if cts_obs
Daniel@0:     for i=onodes(:)'
Daniel@0:       bnet.CPD{i} = gaussian_CPD(bnet, i);
Daniel@0:     end
Daniel@0:   else
Daniel@0:     for i=onodes(:)'
Daniel@0:       bnet.CPD{i} = tabular_CPD(bnet, i);
Daniel@0:     end
Daniel@0:   end
Daniel@0: else
Daniel@0:   bnet.CPD{1} = tabular_CPD(bnet, 1);
Daniel@0:   bnet.CPD{2} = tabular_CPD(bnet, 2);
Daniel@0:   if cts_obs
Daniel@0:     bnet.CPD{3} = gaussian_CPD(bnet, 3);
Daniel@0:   else
Daniel@0:     bnet.CPD{3} = tabular_CPD(bnet, 3);
Daniel@0:   end
Daniel@0: end